Enhancing Guideline-Based Prescribing and Personalized Medication Scheduling

Abstract

Poor communication of medication administration instructions is a preventable cause of medication nonadherence. The Universal Medication Schedule (UMS) framework improves adherence by providing a simplified set of dose timing rules. However, this framework does not readily generalize to individuals with varying daily routines. We propose a point-of-care solution for enhancing guideline-based electronic prescribing and personalizing dose schedules. We describe a JSON-based approach to encode and execute standard treatment guidelines to support electronic prescribing as well as an algorithm for optimizing medication administration schedules based on a patient's daily routine. We evaluated the structure and accuracy of our JavaScript Object Notation (JSON) formalism focusing on Kenya's hypertension treatment guidelines. Our experiments compare the medication schedules generated by our algorithm with those generated by pharmacists. Our findings show that treatment guidelines can be efficiently represented and executed using the JSON formalism, and that different medication administration schedules can be generated automatically and optimized for patients' daily routines.